Model based dynamic positional correction for digital lithography tools

ABSTRACT

The present disclosure generally relates to photolithography systems, and methods for correcting positional errors in photolithography systems. When a photolithography system is first started, the system enters a stabilization period. During the stabilization period, positional readings and data, such as temperature, pressure, and humidity data, are collected as the system prints or exposes a substrate. A model is created based on the collected data and the positional readings. The model is then used to estimate errors in subsequent stabilization periods, and the estimated errors are dynamically corrected during the subsequent stabilization periods.

BACKGROUND Field

Embodiments of the present disclosure generally relate tophotolithography systems, and methods for correcting positional errorsin photolithography systems.

Description of the Related Art

Photolithography is widely used in the manufacturing of semiconductordevices and display devices, such as liquid crystal displays (LCDs).Large area substrates are often utilized in the manufacture of LCDs.LCDs, or flat panels, are commonly used for active matrix displays, suchas computers, touch panel devices, personal digital assistants (PDAs),cell phones, television monitors, and the like. Generally, flat panelsmay include a layer of liquid crystal material forming pixels sandwichedbetween two plates. When power from the power supply is applied acrossthe liquid crystal material, an amount of light passing through theliquid crystal material may be controlled at pixel locations enablingimages to be generated.

Microlithography techniques are generally employed to create electricalfeatures incorporated as part of the liquid crystal material layerforming the pixels. According to this technique, a light-sensitivephotoresist is typically applied to at least one surface of thesubstrate. Then, a pattern generator exposes selected areas of thelight-sensitive photoresist as part of a pattern with light to causechemical changes to the photoresist in the selective areas to preparethese selective areas for subsequent material removal and/or materialaddition processes to create the electrical features.

However, the tool used for such microlithography techniques can take 8hours or longer to fully stabilize the printing and patterning behavior,during which time the patterning of the photoresist may be uneven due tovarious effects, such as thermal variations. The tool comprises numerousheat sources and components that have different conductivitycoefficients and thermal capacitances, each potentially contributing tothe variations causing the uneven patterning, resulting in a negativeeffect on total pitch and overlay correction repeatability.

In order to continue to provide display devices and other devices toconsumers at the prices demanded by consumers, new apparatuses,approaches, and systems are needed to precisely and cost-effectivelycreate patterns on substrates, such as large area substrates.

SUMMARY

The present disclosure generally relates to photolithography systems,and methods for correcting positional errors in photolithographysystems. When a photolithography system is first started, the systementers a stabilization period. During the stabilization period,positional readings and data, such as temperature, pressure, andhumidity data, are collected as the system prints or exposes asubstrate. A model is created based on the collected data and thepositional readings. The model is then used to estimate errors insubsequent stabilization periods, and the estimated errors aredynamically corrected during the subsequent stabilization periods.

In one embodiment, a method comprises starting a photolithography systemand entering a stabilization period, collecting data and positionalreadings as the photolithography system prints during the stabilizationperiod, creating a model based on the data and the positional readings,and dynamically correcting estimate errors during subsequentstabilization periods using the model.

In another embodiment, a method comprises starting a photolithographysystem and entering a stabilization period and collecting temperaturedata and positional readings as the photolithography system printsduring the stabilization period. The temperature data is collectedduring heating and cooling periods. The model further comprises creatinga model based on the temperature data and the positional readings,calibrating the model, using the calibrated model to estimate errors insubsequent stabilization periods, and dynamically correcting theestimated errors during the subsequent stabilization periods.

In yet another embodiment, a method comprises starting aphotolithography system and entering a stabilization period, collectingtemperature data and positional readings as the photolithography systemprints during the stabilization period, creating a model based on thetemperature data and the positional readings, forming an optimizationproblem to determine thermal capacitance and transmission coefficientsof the photolithography system, using the model and optimization problemto estimate errors in subsequent stabilization periods, and dynamicallycorrecting the estimated errors during the subsequent stabilizationperiods.

BRIEF DESCRIPTION OF THE DRAWINGS

So that the manner in which the above recited features of the presentdisclosure can be understood in detail, a more particular description ofthe disclosure, briefly summarized above, may be had by reference toembodiments, some of which are illustrated in the appended drawings. Itis to be noted, however, that the appended drawings illustrate onlyexemplary embodiments and are therefore not to be considered limiting ofits scope, and may admit to other equally effective embodiments.

FIG. 1A is a perspective view of a photolithography system, according toone embodiment.

FIG. 1B is a perspective view of a photolithography system, according toanother embodiment.

FIG. 2 is a perspective schematic view of an image projection apparatus,according to embodiments disclosed herein.

FIG. 3 illustrates a method of modeling and calibrating system behaviorsto estimate positional perturbations occurring during the stabilizationperiod, according to embodiments disclosed herein.

FIGS. 4A-4F illustrate example graphs of data measurements, according toembodiments disclosed herein.

FIG. 5 illustrates an alignment configuration of a first bridgecomponent and a second bridge component each having a plurality of eyesdisposed thereon, according to embodiments disclosed herein.

FIGS. 6A-6C illustrate example graphs of data measurements andpositional readings at a stage speed of 200 mm/s, according toembodiments disclosed herein.

FIGS. 7A-7C illustrate example graphs of data measurements andpositional readings at a stage speed of 100 mm/s, according toembodiments disclosed herein.

To facilitate understanding, identical reference numerals have beenused, where possible, to designate identical elements that are common tothe figures. It is contemplated that elements and features of oneembodiment may be beneficially incorporated in other embodiments withoutfurther recitation.

DETAILED DESCRIPTION

The present disclosure generally relates to photolithography systems,and methods for correcting positional errors in photolithographysystems. When a photolithography system is first started, the systementers a stabilization period. During the stabilization period,positional readings and data, such as temperature, pressure, andhumidity data, are collected as the system prints or exposes asubstrate. A model is created based on the collected data and thepositional readings. The model is then used to estimate errors insubsequent stabilization periods, and the estimated errors aredynamically corrected during the subsequent stabilization periods.

FIG. 1A is a perspective view of a photolithography system 100 accordingto embodiments disclosed herein. The system 100 includes a base frame110, a slab 120, a stage 130, and a processing apparatus 160. The baseframe 110 rests on the floor of a fabrication facility and supports theslab 120. Passive air isolators 112 are positioned between the baseframe 110 and the slab 120. In one embodiment, the slab 120 is amonolithic piece of granite, and the stage 130 is disposed on the slab120. A substrate 140 is supported by the stage 130. A plurality of holes(not shown) are formed in the stage 130 for allowing a plurality of liftpins (not shown) to extend therethrough. In some embodiments, the liftpins rise to an extended position to receive the substrate 140, such asfrom one or more transfer robots (not shown). The one or more transferrobots are used to load and unload a substrate 140 from the stage 130.

The substrate 140 comprises any suitable material, for example AlkalineEarth Boro-Aluminosilicate, used as part of a flat panel display. Inother embodiments, the substrate 140 is made of other materials. In someembodiments, the substrate 140 has a photoresist layer formed thereon. Aphotoresist is sensitive to radiation. A positive photoresist includesportions of the photoresist, which when exposed to radiation, will berespectively soluble to photoresist developer applied to the photoresistafter the pattern is written into the photoresist. A negativephotoresist includes portions of the photoresist, which when exposed toradiation, will be respectively insoluble to photoresist developerapplied to the photoresist after the pattern is written into thephotoresist. The chemical composition of the photoresist determineswhether the photoresist will be a positive photoresist or negativephotoresist. Examples of photoresists include, but are not limited to,at least one of diazonaphthoquinone, a phenol formaldehyde resin,poly(methyl methacrylate), poly(methyl glutarimide), and SU-8. In thismanner, the pattern is created on a surface of the substrate 140 to formthe electronic circuitry.

The system 100 includes a pair of supports 122 and a pair of tracks 124.The pair of supports 122 is disposed on the slab 120, and, in oneembodiment, the slab 120 and the pair of supports 122 are a single pieceof material. The pair of tracks 124 is supported by the pair of thesupports 122, and the stage 130 moves along the tracks 124 in thex-direction. In one embodiment, the pair of tracks 124 is a pair ofparallel magnetic channels. As shown, each track 124 of the pair oftracks 124 is linear. In another embodiment, air bearings are utilizedfor high accurate non-contact motion, and linear motors are configuredto provide the force to move the stage 130 back and forth in thex-direction and the y-direction. In other embodiments, one or more track124 is non-linear. An encoder 126 is coupled to the stage 130 in orderto provide location information to a controller (not shown).

The processing apparatus 160 includes a support 162 and a processingunit 164. The support 162 is disposed on the slab 120 and includes anopening 166 for the stage 130 to pass under the processing unit 164. Theprocessing unit 164 is supported by the support 162. In one embodiment,the processing unit 164 is a pattern generator configured to expose aphotoresist in a photolithography process. In some embodiments, thepattern generator is configured to perform a maskless lithographyprocess. The processing unit 164 includes a plurality of imageprojection apparatus (shown in FIG. 2). In one embodiment, theprocessing unit 164 contains as many as 84 image projection apparatus.Each image projection apparatus is disposed in a case 165. Theprocessing apparatus 160 is useful to perform maskless directpatterning.

During operation, the stage 130 moves in the x-direction from a loadingposition, as shown in FIG. 1A, to a processing position. The processingposition is one or more positions of the stage 130 as the stage 130passes under the processing unit 164. During operation, the stage 130 isbe lifted by a plurality of air bearings (not shown) and moves along thepair of tracks 124 from the loading position to the processing position.A plurality of vertical guide air bearings (not shown) are coupled tothe stage 130 and positioned adjacent an inner wall 128 of each support122 in order to stabilize the movement of the stage 130. The stage 130also moves in the y-direction by moving along a track 150 for processingand/or indexing the substrate 140. The stage 130 is capable ofindependent operation and can scan a substrate 140 in one direction andstep in the other direction.

A metrology system measures the X and Y lateral position coordinates ofeach of the stage 130 in real time so that each of the plurality ofimage projection apparatus can accurately locate the patterns beingwritten in a photoresist covered substrate. The metrology system alsoprovides a real-time measurement of the angular position of each of thestage 130 about the vertical or z-axis. The angular position measurementcan be used to hold the angular position constant during scanning bymeans of a servo mechanism or it can be used to apply corrections to thepositions of the patterns being written on the substrate 140 by theimage projection apparatus 270, shown in FIG. 2. These techniques may beused in combination.

FIG. 1B is a perspective view of a photolithography system 190 accordingto embodiments disclosed herein. The system 190 is similar to the system100; however, the system 190 includes two stages 130. Each of the twostages 130 is capable of independent operation and can scan a substrate140 in one direction and step in the other direction. In someembodiments, when one of the two stages 130 is scanning a substrate 140,another of the two stages 130 is unloading an exposed substrate andloading the next substrate to be exposed.

While FIGS. 1A-1B depict two embodiments of a photolithography system,other systems and configurations are also contemplated herein. Forexample, photolithography systems including any suitable number ofstages are also contemplated.

FIG. 2 is a perspective schematic view of an image projection apparatus270 according to one embodiment, which is useful for a photolithographysystem, such as system 100 or system 190. The image projection apparatus270 includes one or more spatial light modulators 280, an alignment andinspection system 284 including a focus sensor 283 and a camera 285, andprojection optics 286. The components of image projection apparatus varydepending on the spatial light modulator being used. Spatial lightmodulators include, but are not limited to, microLEDs, digitalmicromirror devices (DMDs), liquid crystal displays (LCDs), andvertical-cavity surface-emitting lasers (VCSELs).

In operation, the spatial light modulator 280 is used to modulate one ormore properties of the light, such as amplitude, phase, or polarization,which is projected through the image projection apparatus 270 and to asubstrate, such as the substrate 140. The alignment and inspectionsystem 284 is used for alignment and inspection of the components of theimage projection apparatus 270. In one embodiment, the focus sensor 283includes a plurality of lasers which are directed through the lens ofthe camera 285 and the back through the lens of the camera 285 andimaged onto sensors to detect whether the image projection apparatus 270is in focus. The camera 285 is used to image the substrate, such assubstrate 140, to ensure the alignment of the image projection apparatus270 and photolithography system 100 or 190 is correct or within anpredetermined tolerance. The projection optics 286, such as one or morelenses, is used to project the light onto the substrate, such as thesubstrate 140.

When the photolithography systems 100, 190 are first started, thesystems 100, 190 enter a stabilization period. The stabilization periodis the amount of time it takes the printing and patterning behavior ofthe system to stabilize (i.e., how long it takes the system to warm upcompletely). During the stabilization period of the photolithographysystems 100, 190, various effects and variations occur, such as thermalvariations, which may have a negative effect on total pitch and overlaycorrection repeatability. In some instances, it can take thephotolithography systems 100, 190 eight hours or more to stabilize theprinting and patterning behavior due to the various effects andvariations. Additionally, each system 100, 190 comprises numerous heatsources and components that have different conductivity coefficients andthermal capacitances, each potentially contributing to the variations,making it difficult to monitor the systems 100, 190 rigorously.

To use the systems 100, 190 during the stabilization period to exposesubstrates with precision and accuracy, a model based softwarecorrection may be utilized to correct any errors arising during thestabilization period. The behaviors of the system 100, 190 may bemodeled and calibrated to estimate potential variations occurring duringthe stabilization period, as described below in FIG. 3, to enhance thetotal pitch and overlay correction repeatability. The models may then beused to correct overlay and total pitch errors during subsequentstabilization periods of the systems 100, 190. Utilizing the models fordynamic positional corrections may eliminate or reduce costly hardwaresolutions. Furthermore, the models may be used for dynamic positionalcorrections since the positional corrections are applied to digitalmasks.

FIG. 3 illustrates a method 300 of modeling and calibrating systembehaviors to estimate positional perturbations occurring during thestabilization period, according to embodiments disclosed herein. Method300 may be utilized with the photolithography systems 100, 190 of FIGS.1A and 1B, respectively.

Method 300 beings with operation 302, where a photolithography system isstarted and enters the stabilization period. While in the stabilizationperiod, the printing and patterning behavior of the system may beunstable due to various effects and variations, such as thermal,pressure, and/or humidity variations. The stabilization period is theamount of time it takes the printing and patterning behavior of thesystem to stabilize (i.e., how long it takes the system to warm upcompletely).

In operation 304, data and positional readings are collected as thephotolithography system prints or exposes a substrate during thestabilization period. Data is gathered continuously as the system alignsand exposes a substrate to mimic a production line. In one embodiment,the data collected is temperature data. Temperature data may becollected using one or more temperature sensors disposed near parts ofthe tool known to fluctuation in temperature during heating and cooling,such as an encoder. For example, about 20 temperature sensors may bedisposed on the photolithography tool to collect and monitor thetemperature of the chuck, the encoder, and the bridge/riser, etc.

To collect positional readings, alignment marks (shown in FIG. 5) on acalibration plate or substrate may be captured periodically throughoutthe stabilization period. The calibration plate may be used during thestabilization period as a reference. Additionally or alternatively, anencoder count change with respect to an interferometer reading mayfurther be recorded periodically, where the interferometer is used as areference. Relative positional changes with respect to the referenceused are then recorded.

A pattern printing position on a substrate or calibration plate may beunintentionally perturbed due to thermal effects and fluctuationsoccurring during the stabilization period. As such, perturbations of thepositional readings on the substrate or calibration plate may bedirectly related to fluctuations in the temperature. Other effects maycause perturbations of the positional readings as well, such aspressure, humidity, etc. In such cases, sensors configured to collectpressure data, humidity data, etc., may be utilized instead oftemperature sensors, or in addition to the temperature sensors. However,thermal effects will be used as an example throughout.

FIGS. 4A-4F illustrate example graphs of data measurements andpositional readings. FIGS. 4A-4F are merely examples of datameasurements, and are not intended to be limiting. FIG. 4A illustratesthe change in temperature in Celsius over a period of time for a bridgecomponent and a riser component in a system at a stage speed of 200mm/s. FIG. 4B illustrates the corresponding positional mark along they-axis in micrometers found during heat up for the bridge component andthe riser component at a stage speed of 200 mm/s, which shows thepositional perturbations due to thermal effects. FIG. 4C illustrates thechange in temperature in Celsius over a period of time for a firstbridge component, a second bridge component, and a riser component in asystem at a stage speed of 100 mm/s. FIG. 4D illustrates thecorresponding positional mark along the y-axis in micrometers foundduring heat up for the bridge component and the riser component at astage speed of 100 mm/s, which further shows the y-axis positionalperturbations due to thermal effects. FIG. 4E illustrates a temperaturereading in Celsius over a period of time for master and slave motorsthat move the stage in a photolithography system. FIG. 4F illustrates apositional mark found during a cooling period along the x-axis andy-axis over a period of time. FIGS. 4A-4F illustrate that the behaviorof the system during the stabilization may be representedmathematically.

FIG. 5 illustrates an alignment configuration 500 of a first bridgecomponent 504 and a second bridge component 506 component each having aplurality of eyes 508 disposed thereon, according to one embodiment. Thealignment configuration 500 may be used to collect the data of thegraphs shown in FIGS. 4A-4F above, and to collect the data of the graphsshown in FIGS. 6A-6C and FIGS. 7A-7C shown below. The first bridgecomponent 504 and the second bridge component 506 are disposed above asubstrate or plate 502. The plate 502 comprises a plurality of alignmentmarks 510. While 32 alignment marks 510 are shown, any number ofalignment marks may be utilized. Additionally, while two bridgecomponents 504, 506 are shown, additional bridge components may beutilized in the photolithography system, and each bridge component 504,506 may have more than 4 eyes disposed thereon. The alignmentconfiguration 500 may comprise an exposure unit having a camera (notshown) utilized to collect the positional readings.

FIGS. 6A-6C illustrate example graphs of data measurements andpositional readings at a stage speed of 200 mm/s. FIGS. 7A-7C illustrateexample graphs of data measurements and positional readings at a stagespeed of 100 mm/s. FIGS. 6A-6C and 7A-7C are merely examples of datameasurements, and are not intended to be limiting. The temperature andpositional data displayed in the graphs of FIGS. 6A-6C and FIGS. 7A-7Cmay be collected or measured using any number of temperature sensors andany number of positional marks disposed on a plate.

FIG. 6A and FIG. 7A illustrate a found positional mark along the x-axisin micrometers over a period of time in hours during the stabilizationperiod, which shows the x-axis positional perturbations due to thermaleffects. FIG. 6B and FIG. 7B illustrate the temperature measured at twodifferent locations on a chuck of the system in degrees Celsius over aperiod of time in hours during the stabilization period. FIG. 6C andFIG. 7C illustrate the temperature of a first encoder, a second encoder,and a third encoder of the system in degrees Celsius over a period oftime in hours during the stabilization period.

In operation 306, a model is created based on the collected data andpositional readings. The model may include more than one subset of data,such as having as a model created to take temperature effects, pressureeffects, and/or humidity effects, etc. into consideration. When creatingthe model, an assumption is made that the systems are linear or weaklynon-linear. The model may use effective thermal capacitance andtransmission as model parameters. The model may further take intoaccount that the systems operate in a repetitive fashion. The datagraphed in one or more of the FIGS. 4A-4F, 6A-6C, and 7A-7C may be usedindependently or in combination to help create the model.

Additionally, a dynamic eye-to-eye and/or bridge-to-bridge model may beincorporated into the created model. The model may capture the variationof eye centers with respect to one another or the drift of theseparation between bridges, such as the first and second bridgecomponents 504, 506 and the eyes 508 of FIG. 5. The dynamic eye-to-eyeand/or bridge-to-bridge models may be empirical models, and the modelparameters may be calibrated based on the experimental results.

Operations 302 and 304 may be repeated one or more times to collect agreater amount of data to use to create the model. The model may be acascaded transient model that can relate positional errors to multiplesensor readings. The transient response of each component is determinedby the component's thermal capacitance or mass, as well as thermaltransmission properties. The cascaded empirical models may then be usedto represent thermal effects in the system.

The model is formed using the following variables or parameters: wherethe positional readings should be at (x, y) during the stabilizationperiod without thermal effects, where the positional readings actuallyare (x′, y′) due to thermal effects, approximations of whereperturbations of the positional readings are (Δx, Δy) (i.e., thedifference between positional readings without thermal effects and thepositional readings with thermal effects), an initial temperature (T₀),and the change in temperature from the initial temperature reading (ΔT).In one embodiment, at least the initial temperature (T₀) and the changein temperature from the initial temperature reading (ΔT) must be knownin order to form the model. To approximate the where the positionalreadings actually are due to thermal effects, equations 1-4 may be used.

x′=x+Δx(x, y, ΔT , T ₀ )  Equation 1

y′=y+Δy(x, y, ΔT , T ₀ )  Equation 2

Δx(x, y, ΔT , T ₀ )≈Σ_(i=1) ^(N)∝_(xi)(ΔT _(i) , T _(i0))∅_(xi)(x,y)  Equation 3

Δy(x, y, ΔT , T ₀ )≈Σ_(i=1) ^(N)∝_(yi)(ΔT _(i) , T _(i0))∅_(yi)(x,y)  Equation 4

In equation 3 and equation 4, ϕ is a spatial mode, and a is a modeltransformation between the temperature and overall positional changes.The positional perturbations (Δx, Δy) are formulated as a function ofall temperature sensor readings, including both previous readings andcurrent readings.

In operation 308, the model is calibrated. Calibrating the model maycomprise continuously operating the photolithography system to model thestabilization period, and keeping the photolithography system idlefollowing the stabilization period to model a cool down period. Themodel is further calibrated by forming an optimization problem. Theoptimization problem is formed to obtain the model parameters tominimize an overall cost function (C) (shown in equation 7 below). Costis defined as the summation of the difference between the measurementand the model prediction at multiple positions (x, y) and multipletemperature conditions to represent the transition. The optimizationproblem may be formed to minimize the cost function.

The optimization problem is formed to determine multiple thermalcapacitance and transmission coefficients of the system during thestabilization period. The input of the optimizer is the collectedtemperature readings and corresponding positional errors at multiplepositions. The output of the optimizer is a set of thermal capacitanceand transmission coefficients. The optimizer may minimize the differencebetween a measured position on the substrate and the model estimatedposition. The model may be calibrated using equations 5-7. Equations 5and 6 are used for model prediction errors, where x′_(meas) andy′_(meas) are the measured positional changes due to thermal effects.Equation 7 is the cost function, where K is the number of collected dataand L is the number of calibrated parameters.

$\begin{matrix}{\mspace{79mu} {{ɛ_{x}\left( {x,y} \right)} = {x_{meas}^{\prime} - x^{\prime}}}} & {{Equation}\mspace{14mu} 5} \\{\mspace{79mu} {{ɛ_{y}\left( {x,y} \right)} = {y_{meas}^{\prime} - y^{\prime}}}} & {{Equation}\mspace{14mu} 6} \\{C = {\sqrt{\frac{1}{LK}\left( {\sum_{k = 1}^{K}{\sum_{l = 1}^{L}ɛ_{x}^{k,l}}} \right)^{2}} + \sqrt{\frac{1}{LK}\left( {\Sigma_{k = 1}^{K}\Sigma_{l = 1}^{L}ɛ_{y}^{k,l}} \right)^{2}}}} & {{Equation}\mspace{14mu} 7}\end{matrix}$

In operation 310, the calibrated model is used to estimate errors insubsequent stabilization periods, and the estimated errors in thesubsequent stabilization periods are dynamically corrected. After themodel is calibrated, the model may be used during subsequentstabilization periods to correct predicted positional errors andperturbations due to the thermal effects. As noted above, thermaleffects are only one type effect or variation that may be considered,and is not intended to be a limiting example. The estimated positionalerrors during the stabilization period may be corrected by dynamicallymodifying the digital mask of the photolithography system on-the-fly,and are not corrections or changes made to the physical photolithographysystem itself. The corrections of the estimated positional errors may bedynamic digital corrections applied per plate or per substrate duringexposure to the digital mask.

The calibrated model may further be used to monitor the stability of thephotolithography system. An alignment model modeling the alignment ofthe digital mask may be formed based on the calibrated model. Thealignment model may then be compared to the alignment of the digitalmask during subsequent stabilization periods. The comparison of thealignment model to the alignment during subsequent stabilization periodsmay be used to determine a similarity metric. The similarity metric maybe used to determine whether the subsequent stabilization period is thesame as the initial stabilization period used to create the model (i.e.,whether the subsequent stabilization period experiences the samepositional perturbations as the initial stabilization period). Thesimilarity metric may determine the stability of the system bydetermining whether the same positional perturbations are repeatedlyoccurring. If the system is stable, potential positional errors will beeasier to estimate, as the same errors will be occurring repeatedly atthe same point of time during the stabilization periods.

In at least one implementation, the model may be a machine learningmodel or problem with model guidance, such as neural-networks. Forexample, if a large amount of data is available, the photolithographysystems may use the plurality of sensors and the large amount of data toproactively correct positional perturbations or errors that have a highfrequency of occurrence before the errors occur in subsequentstabilization periods. The systems may use the data, the sensors, and/orthe model to estimate or determine errors that have a high frequency ofoccurrence, and compensate for the potential errors before the errorsoccur. After compensating for the perturbations or errors having a highfrequency of occurrence, the systems may further compare the currentprinting positions to the model to determine whether the compensationactually corrected the potential error or not, and may make additionaladjustments as needed. Thus, instead of correcting errors on-the-fly asthey occur, the systems may use the machine learning algorithms toproactively compensate for the potential errors prior to occurrence.

Using the above described methods, photolithography system behaviors maybe accurately modeled and calibrated to estimate positionalperturbations occurring during the stabilization period, which enhancesthe total pitch and overlay correction repeatability. The models maythen be used to correct overlay and total pitch errors on-the-fly duringsubsequent stabilization periods of the systems by adjusting the digitalmask. Additionally, if a large amount of data is available to thesystems, the systems may proactively compensate for potential positionalperturbations or errors prior to the occurrence of the errors using amachine learning model with model guidance.

Utilizing the models for dynamic positional corrections may eliminate orreduce costly hardware solutions. The models may easily be used fordynamic positional corrections since the positional corrections areapplied to digital masks. Moreover, since the models are asoftware-based solution, new model forms can be developed to include neweffects that were not previously included or covered, or to includeadditional sensors that were not initially available. As such, thephotolithography systems may be accurately utilized for exposure ofplates or substrates during their stabilization periods.

While the foregoing is directed to embodiments of the presentdisclosure, other and further embodiments of the disclosure may bedevised without departing from the basic scope thereof, and the scopethereof is determined by the claims that follow.

What is claimed is:
 1. A method, comprising: starting a photolithographysystem and entering a stabilization period; collecting data andpositional readings as the photolithography system prints during thestabilization period; creating a model based on the data and thepositional readings; and dynamically correcting estimate errors duringsubsequent stabilization periods using the model.
 2. The method of claim1, wherein the data collected is temperature data.
 3. The method ofclaim 2, wherein the temperature data is collected using a plurality oftemperature sensors disposed throughout the photolithography system. 4.The method of claim 2, wherein the model is formed using one or moreparameters selected from the following group: where the positionalreadings should be at during the stabilization period without thermaleffects, where the positional readings actually are due to thermaleffects, approximations of perturbations of the positional readings, aninitial temperature of the photolithography system, and a measuredchange in temperature from the initial temperature after a predeterminedamount of time has passed.
 5. The method of claim 2, wherein thetemperature data is collected during heating and cooling periods of thestabilization period.
 6. The method of claim 1, wherein the datacollected is pressure data. The method of claim 1, wherein the datacollected is humidity data.
 8. The method of claim 1, wherein the modelis a set of cascaded transient models.
 9. A method, comprising: startinga photolithography system and entering a stabilization period;collecting temperature data and positional readings as thephotolithography system prints during the stabilization period, whereinthe temperature data is collected during heating and cooling periods;creating a model based on the temperature data and the positionalreadings; calibrating the model; using the calibrated model to estimateerrors in subsequent stabilization periods; and dynamically correctingthe estimated errors during the subsequent stabilization periods. 10.The method of claim 9, wherein pressure data is further collected, andthe model is created based on the pressure data.
 11. The method of claim9, wherein humidity data is further collected, and the model is createdbased on the humidity data.
 12. The method of claim 9, wherein the modelis a set of cascaded transient models.
 13. The method of claim 9,wherein the temperature data is collected using a plurality oftemperature sensors disposed throughout the photolithography system. 14.The method of claim 9, wherein the model is formed using one or moreparameters selected from the following group: where the positionalreadings should be at during the stabilization period without thermaleffects, where the positional readings actually are due to thermaleffects, approximations of perturbations of the positional readings, aninitial temperature of the photolithography system, and a measuredchange in temperature from the initial temperature after a predeterminedamount of time has passed.
 15. A method, comprising: starting aphotolithography system and entering a stabilization period; collectingtemperature data and positional readings as the photolithography systemprints during the stabilization period; creating a model based on thetemperature data and the positional readings; forming an optimizationproblem to determine thermal capacitance and transmission coefficientsof the photolithography system; using the model and optimization problemto estimate errors in subsequent stabilization periods; and dynamicallycorrecting the estimated errors during the subsequent stabilizationperiods.
 16. The method of claim 15, wherein pressure data is furthercollected, and the model is created based on the pressure data.
 17. Themethod of claim 15, wherein humidity data is further collected, and themodel is created based on the humidity data.
 18. The method of claim 15,wherein the model is a set of cascaded transient models.
 19. The methodof claim 15, wherein the temperature data is collected during heatingand cooling periods of the stabilization period.
 20. The method of claim15, wherein the model is formed using one or more parameters selectedfrom the following group: where the positional readings should be atduring the stabilization period without thermal effects, where thepositional readings actually are due to thermal effects, approximationsof perturbations of the positional readings, an initial temperature ofthe photolithography system, and a measured change in temperature fromthe initial temperature after a predetermined amount of time has passed.